| dc.contributor.author | Fu, Michael C. | |
| dc.date.accessioned | 2005-07-01T12:31:02Z | |
| dc.date.available | 2005-07-01T12:31:02Z | |
| dc.date.issued | 2005-07-01T12:31:02Z | |
| dc.identifier.uri | http://hdl.handle.net/1903/2298 | |
| dc.description | This is a pre-print version of Chapter 19 in
Handbooks in Operations Research and
Management Science: Simulation,
S.G. Henderson and B.L. Nelson, eds., Elsevier. | en |
| dc.description.abstract | We consider the problem of efficiently estimating gradients
from stochastic simulation.
Although the primary motivation is their use in simulation optimization,
the resulting estimators can also be useful in other ways,
e.g., sensitivity analysis.
The main approaches described are finite differences
(including simultaneous perturbations),
perturbation analysis,
the likelihood ratio/score function method,
and the use of weak derivatives. | en |
| dc.description.sponsorship | This work was supported in part by the National Science Foundation
under Grants DMI 9988867 and DMI 0323220,
and by the Air Force Office of Scientific Research
under Grants F496200110161 and FA95500410210. | en |
| dc.format.extent | 303248 bytes | |
| dc.format.mimetype | application/pdf | |
| dc.language.iso | en_US | |
| dc.subject | simulation | en |
| dc.subject | gradient estimation | en |
| dc.subject | perturbation analysis | en |
| dc.subject | likelihood ratio method | en |
| dc.subject | weak derivatives | en |
| dc.title | Stochastic Gradient Estimation | en |
| dc.type | Book chapter | en |
| dc.relation.isAvailableAt | Robert H. Smith School of Business | en_us |
| dc.relation.isAvailableAt | Decision & Information Technologies | en_us |
| dc.relation.isAvailableAt | Digital Repository at the University of Maryland | en_us |
| dc.relation.isAvailableAt | University of Maryland (College Park, Md.) | en_us |
